<p>Prediction of the rapidly fading envelope of a mobile radio channel enables a number of capacity improving techniques like fast resource allocation and fast link adaptation. This thesis deals with linear prediction of the complex impulse response of a channel and unbiased quadratic prediction of the power. The design and performance of these predictors depend heavily on the correlation properties of the channel. Models for a channelwhere the multipath is caused by clusters of scatterers are studied. The correlation for the contribution from a cluster can be approximated as a damped complex sinusoid. A suitable model for the dynamics of the channel is an ARMA-process. This motivates the use of linear predictors.</p><p>A limiting factor in the prediction are the estimation errors on the observed channels. This estimation error, caused by measurement noise and time variation, is analyzed for a block based least squares algorithm which operates on a Jakes channel model. Efficient noise reduction on the estimated channel impulse responses can be obtained with Wienersmoothers that are based on simple models for the dynamics of the channel combined with estimates of the variance of the estimation error.</p><p>Power prediction that is based on the squared magnitude of linear prediction of the taps will be biased. Hence, a bias compensated power predictor is proposed and the optimal prediction coefficients are derived for the Rayleigh fading channel. The corresponding probability density functions for the predicted power are also derived. A performance evaluation of the prediction algorithm is carried out on measured broadband mobile radio channels. The performance is highly dependent on the variance of the estimation error and the dynamics of the individual taps.</p>
| Identifer | oai:union.ndltd.org:UPSALLA/oai:DiVA.org:uu-2677 |
| Date | January 2002 |
| Creators | Ekman, Torbjörn |
| Publisher | Uppsala University, Signals and Systems Group, Uppsala : Institutionen för materialvetenskap |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Doctoral thesis, monograph, text |
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